共查询到18条相似文献,搜索用时 171 毫秒
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BP神经网络(Back Propagation Neural Network,BP-NN)具有良好的自学习能力以及自适应和泛化能力,但运算过程中容易陷入局部极小值,同时隐含层节点数的选择也影响着诊断的效果。文中根据经验公式缩小隐层节点数范围,在小范围里寻找最优的隐层节点数。根据遗传算法(Genetic Algorithm,GA)具有全局寻优的特点,用遗传算法优化BP神经网络训练的初始权值阈值,可以避免BP神经网络陷入局部极小的问题。但是,传统遗传算法也有自身的缺点,其在全局寻优的过程中,易陷入“早熟”的问题。为了解决传统遗传算法“早熟”现象,文中提出了一种协同进化的遗传算法,即使用3个种群同时进化的遗传算法,协同进化遗传算法不但可以避免传统遗传算法的“早熟”问题,而且可以加强局部搜索提高运行效率。将协同进化遗传算法应用到BP神经网络中,仿真结果表明,该方法可以准确有效地诊断出变电站故障元件,提高变电站故障诊断过程中的容错性及效果。 相似文献
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为提高混合无线传感器网络(WSNs)的覆盖率,将改进的遗传算法应用到WSNs覆盖优化中,通过合理调整移动节点的位置来提高网络覆盖率。针对传统群体智能算法易“早熟”,最大迭代次数需试探设定等缺陷,提出了基于多个种群并行优化的改进遗传算法。多个种群之间并不独立,而是通过移民算子相互联系;分别利用人工选择算子与精华种群选择并记录各个种群每一代最优染色体;并利用精华种群中保存的最优染色体设计出新的进化终止条件。仿真结果表明,改进的遗传算法不仅无需设定最大迭代次数而且收敛速度快,更兼有效地提高了WSNs的覆盖率 相似文献
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为了解决脉冲整形实验中经常碰到的遗传算法收敛速度慢,早熟等问题,我们对传统的遗传算法进行了几点改进,例如:将两个个体间的欧几里得距离作为判断是否进行交叉操作的判据之一,而不再仅仅依靠个体的适应度值(fitness),这样能有效地保持种群的基因多样性,提高交叉算子的效率;第二,引入多个交叉算子共同作用于种群. 由于算子的组合效应,共同作用产生的子代适应度值要优于任何一个算子单独作用时产生的子代适应度值. 因而可以产生更大的探索范围,防止算法收敛在某个局部最优解;第三,为了提高收敛速度,我们提出一种新的插值方式:非线性插值,即依据频谱的强度大小决定插值点的密度. 我们初步将此改进算法应用到飞秒整形光路输出光的相位补偿实验中,得到了比较令人满意的结果. 相似文献
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针对柔性作业车间调度问题中最大完工时间、机器最大负荷和总机器负荷三项性能指标,提出一种改进的自适应交叉和变异的混合遗传算法。在基本遗传算法染色体编码的基础上,设计一种基于海明距离的调度个体差异判别方法,并通过自适应交叉阈值和动态变异概率计算提高遗传算法整个种群调度个体的多样性,防止算法过早的进入早熟。在遗传算法进化期间,对每个调度个体的进化采用变邻域搜索算法,扩大调度个体的邻域搜索范围。最后,使用文献中相同的调度实例将本文的计算结果与其它文献中的测试结果进行比较,验证了所提出的算法的可行性和有效性。 相似文献
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应用自适应遗传算法的气动优化设计 总被引:7,自引:1,他引:6
对简单遗传算法(SGA)加以改进,形成了气动优化设计中的自适应遗传算法(SAGA)。采用实数编码来表示种群中的个体,不需要进行二进制的编码和解码操作,并针对具体问题设计了杂交和变异算子,提高了优化设计的质量和效率。最后分别以翼型和机翼为例,应用自适应遗传算法对跨音速翼型和机翼的升阻比进行优化设计。 相似文献
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This paper analyzes the spatial evolution character of multi-objective evolutionary algorithms using self-organized criticality theory. The spatial evolution character is modeled by the statistical property of crowding distance, which displays a scale-free feature and a power-law distribution. We propose that the evolutional rule of multi-objective optimization algorithms is a self-organized state transition from an initial scale-free state to a final scale-free state. The target is to get close to a critical state representing the true Pareto-optimal front. Besides, the anti-Matthew effect is the internal incentive factor of most strategies. The final scale-free state reflects the quality of the final Pareto-optimal front. The speed of the state transition reflects the efficiency of the algorithm. We simulate the spatial evolution characters of three typical multi-objective evolutionary algorithms representing three fields, i.e., Genetic Algorithm, Differential Evolution and the Artificial Immune System algorithm. The results prove that the model and the explanation are effective for analyzing the evolutional rule of multi-objective evolutionary algorithms. 相似文献
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针对一类数学模型未知且存在时变时滞的复杂系统,提出一种基于遗传算法参数整定的灰色预测控制方法。该方法采用BP神经网络对系统的时变时滞进行辨识,利用灰色预测算法对系统的输出进行预测,进而使用基于遗传算法整定PID控制器对系统进行输出反馈控制。该方法将灰色预测算法与遗传算法相结合,有效提高了控制器的自适应性。通过仿真实例,结果表明该方法能够对具有大时滞、大惯性、模型不确定等特点的复杂系统进行有效地控制。该方法是可行的、有效的。 相似文献
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遗传算法具有很强的自适应性、鲁棒性和全局搜索能力,但其局部搜索能力相对较弱,计算后期易出现进化缓慢、过早收敛等问题,蚁群算法是近几年迅速发展起来的一种新的全局优化算法,具有正反馈机制,但是计算初期由于信息素差别小,初始收敛速度较慢.本文将这两种优化方法结合起来,充分发挥各自的优势,形成了遗传-蚁群混合算法,并选用测试函数对算法的优化性能作了对比计算,最后以高温超导匀场磁体为实际应用目标,以绕制磁体所用超导带长度为目标函数对磁体结构进行优化设计,优化方案比原始方案节省7.32%的超导带材用量. 相似文献
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Algebraic dynamics solutions and algebraic dynamics algorithm for nonlinear ordinary differential equations 总被引:2,自引:2,他引:0
The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms
of preserving local differential structure and approximating global integration structure of the dynamical system. The ordinary
differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,
and a new algorithm—algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential
equations by the algebraic dynamics method. In the new algorithm, the time evolution of the ordinary differential system is
described locally by the time translation operator and globally by the time evolution operator. The exact analytical piece-like
solution of the ordinary differential equations is expressed in terms of Taylor series with a local convergent radius, and
its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm
and Symplectic Geometric Algorithm. 相似文献
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The hybrid CMOS molecular (CMOL) circuit, which combines complementary metal–oxide–semiconductor (CMOS) components with nanoscale wires and switches, can exhibit significantly improved performance. In CMOL circuits, the nanodevices, which are called cells, should be placed appropriately and are connected by nanowires. The cells should be connected such that they follow the shortest path. This paper presents an efficient method of cell allocation in CMOL circuits with the hybrid CMOS/nanodevice structure; the method is based on a cultural algorithm with chaotic behavior. The optimal model of cell allocation is derived, and the coding of an individual representing a cell allocation is described. Then the cultural algorithm with chaotic behavior is designed to solve the optimal model. The cultural algorithm consists of a population space, a belief space, and a protocol that describes how knowledge is exchanged between the population and belief spaces. In this paper, the evolutionary processes of the population space employ a genetic algorithm in which three populations undergo parallel evolution. The evolutionary processes of the belief space use a chaotic ant colony algorithm. Extensive experiments on cell allocation in benchmark circuits showed that a low area usage can be obtained using the proposed method, and the computation time can be reduced greatly compared to that of a conventional genetic algorithm. 相似文献
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